A function that joins separate marginal distributions into a joint distribution, isolating the dependence structure. It lets the shape of each variable and their dependence be modelled separately.
Where you will meet it
Copula comes up in 17 learning outcome notes across the FRM syllabus.
A few of them:
Measuring Returns, Volatility, and Correlation FRM Part I · Quantitative analysis
“It underlies Gaussian copula models of default correlation.”
Measuring Credit Risk FRM Part I · Valuation and risk models
“A copula combines separate marginal distributions into one joint distribution while controlling the dependence.”
Credit Risk FRM Part II · Credit risk measurement and management
“The Gaussian copula links individually estimated distributions of time to default into one joint distribution.”
Parametric Approaches (II): Extreme Value FRM Part II · Market risk measurement and management
“It then models the dependence between extremes separately, for example with an extreme value copula or a tail dependence coefficient.”
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